6/9/2026
HARP: Efficient Data Selection for Finetuning Large Language Models
HARP presents efficient data selection for LLM finetuning, balancing scalability and downstream utility through train-free and train-based selection methods.
6/9/2026
Vessel Traffic Flow Prediction on Sparse Data via Spatio-Temporal Graph Neural Networks with a Learnable Tweedie Head
Spatio-temporal graph neural networks with learnable Tweedie head for vessel traffic flow prediction on sparse maritime data with zero-inflated distributions.
6/9/2026
DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems
DSFNet proposes dual-domain spectral operators for multi-modality spatio-temporal forecasting in urban traffic systems, modeling coupling relationships between modalities.
6/9/2026
Adversarial Robustness of Activation Steering in Large Language Models
First systematic evaluation of activation steering robustness in LLMs under adversarial text perturbations, testing four extraction methods and three attack strategies.
6/9/2026
Pharmacogenomic Knowledge Graph Augmentation for Graph Neural Network-Based Drug-Drug Interaction Prediction
GNNs augmented with pharmacogenomic knowledge for predicting drug-drug interactions.
6/9/2026
EssentialGIN: a new approach for gene essentiality prediction based on graph isomorphism neural networks
Graph isomorphism neural networks applied to gene essentiality prediction in computational biology.
6/9/2026
EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning
Proposes surrogate-assisted evolutionary algorithm for client selection in federated learning to improve convergence and robustness.
6/9/2026
How Much Dense Attention is Necessary? Oracle-Guided Sparse Prefill for Full/GQA Layers in Hybrid Long-Context Models
Research on optimizing dense attention in long-context LLMs using oracle-guided sparse prefill to reduce computational costs while preserving task performance.
6/9/2026
FunctionEvolve: Structure-Guided Symbolic Regression with LLMs
FunctionEvolve method combining LLM guidance with symbolic structure for symbolic regression, discovering scientific laws from data with domain-informed search.
6/9/2026
SAW: Stage-Aware Dynamic Weighting for Multi-Objective Reinforcement Learning in Large Language Models
SAW stage-aware dynamic weighting technique for multi-objective RL in LLMs, addressing asynchronous reward learning across objectives.
6/9/2026
Decoding Naturalistic Emotion Dynamics from the Brain: An LLM-Enhanced Regression Framework
Multi-target regression framework with LLM enhancement for decoding continuous emotion dynamics from brain signals as alternative to discrete classification.
6/9/2026
WhiFlash: Accelerating Speculative Decoding with Token-Level Cross-Paradigm Routing
WhiFlash accelerates speculative decoding via token-level cross-paradigm routing, switching between autoregressive and diffusion drafting based on token type.
6/9/2026
Rosetta Memory: Adaptive Memory for Cross-LLM Agents
Rosetta Memory system providing adaptive memory for multi-LLM agents, enabling persistence and experience accumulation across different LLM backends.
6/9/2026
Beyond Accuracy: Interpreting Topic Representation in Suicide Ideation Detection Models
Interpretability analysis of suicide ideation detection models, examining internal representations of psychological risk factors beyond accuracy metrics.
6/9/2026
A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction
GTF-Net geometry-aware triplane field network for vehicle aerodynamic CFD prediction as faster ML alternative to high-fidelity simulation.
6/9/2026
Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity
SySRs bandit algorithm reducing LLM evaluation costs by exploiting model similarity and adaptively allocating evaluation budget.
6/9/2026
scCBGM: Interpretable Single-Cell Counterfactual Editing
scCBGM framework for interpretable counterfactual editing of single cells using concept bottleneck generative models for cellular phenotype analysis.
6/9/2026
Contrast encodes inductive bias: separating slow noise from dynamics in predictive representation learning
Study showing contrastive learning methods confuse slow noise with dynamics, proposing fixes for predictive representation learning frameworks like JEPA.
6/9/2026
A Framework for Evaluating and Benchmarking Concept Drift Detection Methods
Framework and benchmarking suite for evaluating concept drift detection methods in data stream mining with standardized metrics.
6/9/2026
Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Games
Study of Byzantine adversarial resilience in multi-agent LLM coordination games, examining robustness of communication protocols under attacks.
6/9/2026
Mitigating the Contractivity Trap in Diffusion ODEs via Stein Stabilization
SteinDiff method addressing contractivity trap in diffusion model inference via large-step ODEs with Stein stabilization.
6/9/2026
Teacher-Free Self-Training Amplifies but Does Not Compound: A Pass@$K$ Crossover on a Free-Verifier Domain
Teacher-free self-training study examining whether language models acquire new capabilities or improve expression of existing ones using exact verifiers.
6/9/2026
Instrumented data for causal scientific machine learning
Instrumented data approach for scientific machine learning combining observational data with mechanistic models and uncertainty quantification.
6/9/2026
Still: Amortized KV Cache Compaction in a Single Forward Pass
STILL method for KV cache compaction in language models during inference, addressing memory bottleneck for long-horizon deployment.
6/9/2026
Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency
Pipeline parallelism optimization technique (PACI) for training large neural networks with bounded weight inconsistency, reducing memory bubbles.
6/9/2026
Strained Coherence: A Pre-Failure Signal in Coding Agent Execution Trajectories
Research on strained coherence failure mode in LLM-based coding agents where agents acknowledge problems but proceed anyway, related to reward hacking.
6/9/2026
Partially Performative Prediction
Framework studying feedback loops when ML models deployed in real-world settings, examining endogenous distribution shifts.
6/9/2026
Temporal Coverage over Density: Parsimonious Training-Set Design for ML Climate Downscaling
ML-based climate downscaling using strategic training-set design to balance forced climate response and internal variability.
6/9/2026
Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimes
Study of derivative-controlled neural networks evaluating generalization across different data regimes with per-layer Jacobian penalties.
6/9/2026
CAAL: Contextual Bandits based Online Hand-Craft Active Learning Strategy Selection
Contextual bandits approach for selecting active learning strategies dynamically without relying solely on labeled data feedback.
6/9/2026
The Easy, the Hard, and the Learnable: Confidence and Difficulty-Adaptive Policy Optimization for LLM Reasoning
RL method for improving LLM reasoning via verifiable rewards, analyzing confidence inflation and inefficient compute allocation in standard GRPO training.
6/9/2026
Minibatch Selection via Partition Matroid Constrained Gradient Matching
Cross-domain minibatch selection method using gradient matching and partition matroid constraints for training LLMs on heterogeneous data.
6/9/2026
Overcoming the Limits of Finite Difference Method; Physics-Informed Neural Network for Noisy High-Dimensional Heat Diffusion
Physics-informed neural network framework for solving high-dimensional heat diffusion under noisy boundary conditions.
6/9/2026
Enhancing AI Interpretability and Safety through Localised Architectures
Discussion of localized architectures for improving interpretability and safety of large language models and reasoning models.
6/9/2026
Evaluating the Impact of Task Granularity on Catastrophic Forgetting in Continual Learning
Study investigating task ordering effects on catastrophic forgetting in continual learning, comparing general-to-specific versus mixed learning orders.
6/9/2026
Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure
Semantic reliability certification framework for multi-agent LLM systems in autonomous cloud operations, addressing operationally unsafe mutations.
6/9/2026
CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learning
Privacy-preserving vertical federated learning using causal representation learning to defend against sample reconstruction attacks.
6/9/2026
Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization
High-probability regret bounds for online convex optimization with noise-adaptive guarantees and strongly convex losses.
6/9/2026
SafeECGMatch: Calibration-Aware Joint Frequency and Time Space Semi-Supervised Learning for Open-Set ECG Classification
Semi-supervised learning approach for ECG classification handling label scarcity and out-of-distribution anomalies in clinical settings.
6/9/2026
When Behavioral Safety Evaluation Fails: A Representation-Level Perspective
Analysis of LLM safety evaluation gaps, formalizing audit gap between behavioral safety and representation-level robustness under intervention.
6/9/2026
Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense
Study of graph reconstruction attacks on GNNs and defenses against model inversion attacks that reconstruct training adjacency from trained models.
6/9/2026
DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination
Multi-agent LLM coordination framework using entropy-regularized equilibrium selection to improve stability and performance beyond single-model sampling.
6/9/2026
ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning
Framework for improving LLM reasoning via reinforcement learning by integrating token-level confidence signals from model log-probabilities into RLVR training.
6/9/2026
Constraint-Aware Optimization for Robust Protein Stability Prediction
Protein stability prediction using multimodal models combining protein language models with inverse-folding representations, addressing out-of-distribution robustness.
6/9/2026
LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection
Log anomaly detection system using GPT-Neo (1.3B) fine-tuned with reinforcement learning (PPO) and position-aware rewards for real-time infrastructure monitoring.
6/9/2026
Have I Solved This Before? Retrieving Similar Segmentation Problems for Evolutionary Learning
Evolutionary learning approach for segmentation problems in manufacturing monitoring systems, investigating retrieval of similar past problems.
6/9/2026
AttentionCap: Transformer Based Capacitance Matrix Learning Toward Full-Chip Extraction
Deep learning approach using transformers for capacitance matrix extraction in chip design, addressing limitations of rule-based pattern matching at advanced nodes.
6/9/2026
Explaining Data Mixing Scaling Laws
Theoretical framework explaining empirical scaling laws for model performance on multi-domain data mixtures, extending neural scaling law perspectives to multi-domain settings.
6/9/2026
Neural Field Tokenizations with Hierarchy and Spatial Locality Priors
Neural field tokenization method with hierarchical and spatial locality priors for efficient feed-forward encoding across modalities.
6/9/2026
Public Machine Learning Solver Framework for Novices in the Machine Learning Domain
Platform combining algorithm selection guidance with decision-support systems to help non-experts solve machine learning problems.